class_docstr: Optimizer that implements the Adagrad algorithm.
  
  Adagrad is an optimizer with parameter-specific learning rates,
  which are adapted relative to how frequently a parameter gets
  updated during training. The more updates a parameter receives,
  the smaller the updates.
  
  Args:
      learning_rate: A float, a
          `keras.optimizers.schedules.LearningRateSchedule` instance, or
          a callable that takes no arguments and returns the actual value to
          use. The learning rate. Defaults to `0.001`. Note that `Adagrad`
          tends to benefit from higher initial learning rate values compared
          to other optimizers. To match the exact form in the original paper,
          use `1.0`.
      initial_accumulator_value: Floating point value. Starting value for the
          accumulators (per-parameter momentum values). Must be non-negative.
      epsilon: Small floating point value for maintaining numerical stability.
      {{base_optimizer_keyword_args}}
  
  Reference:
  
  - [Duchi et al., 2011](
      http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
code: def __init__(
      self,
      learning_rate=0.001,
      initial_accumulator_value=0.1,
      epsilon=1e-7,
      weight_decay=None,
      clipnorm=None,
      clipvalue=None,
      global_clipnorm=None,
      use_ema=False,
      ema_momentum=0.99,
      ema_overwrite_frequency=None,
      name="adagrad",
      **kwargs,
  ):
      super().__init__(
          learning_rate=learning_rate,
          weight_decay=weight_decay,
          clipnorm=clipnorm,
          clipvalue=clipvalue,
          global_clipnorm=global_clipnorm,
          use_ema=use_ema,
          ema_momentum=ema_momentum,
          ema_overwrite_frequency=ema_overwrite_frequency,
          name=name,
          **kwargs,
      )
      self.initial_accumulator_value = initial_accumulator_value
      self.epsilon = epsilon
  
file_path: keras/keras/optimizers/adagrad.py
imports: from keras import initializers
  from keras import ops
  from keras.api_export import keras_export
  from keras.optimizers import optimizer
label_desc: Use image node_id 1 to create a new Adagrad object from inherited base classes: optimizer with example: obj = Adagrad(learning_rate, initial_accumulator_value, epsilon, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, name)